June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Automated segmentation of retinal nerve fiber layer excluding retinal blood vessels: integrating OCT and OCT Angiography.
Author Affiliations & Notes
  • Matteo Airaldi
    Eye Clinic, Department of Biomedical and Clinical Science "Luigi Sacco", Universita degli Studi di Milano, Milano, Lombardia, Italy
  • Jonathan D Oakley
    Voxeleron LLC, San Franscico, CA, USA, San Francisco, California, United States
  • Sara Bochicchio
    Eye Clinic, Department of Biomedical and Clinical Science "Luigi Sacco", Universita degli Studi di Milano, Milano, Lombardia, Italy
  • Angelica Dipinto
    Eye Clinic, Department of Biomedical and Clinical Science "Luigi Sacco", Universita degli Studi di Milano, Milano, Lombardia, Italy
  • Simona Prandoni
    Ophthalmic Hospital, ASST Fatebenefratelli-Sacco, Universita degli Studi di Milano, Milano, Lombardia, Italy
  • Giovanni Staurenghi
    Eye Clinic, Department of Biomedical and Clinical Science "Luigi Sacco", Universita degli Studi di Milano, Milano, Lombardia, Italy
  • Giacinto Triolo
    Ophthalmic Hospital, ASST Fatebenefratelli-Sacco, Universita degli Studi di Milano, Milano, Lombardia, Italy
  • Footnotes
    Commercial Relationships   Matteo Airaldi None; Jonathan Oakley Voxeleron LLC, Code E (Employment), Voxeleron LLC, Code P (Patent); Sara Bochicchio None; Angelica Dipinto None; Simona Prandoni None; Giovanni Staurenghi Heidelberg Engineering, Centervue, Carl Zeiss, Apellis, Allergan, Bayer, Boheringer, Genentech, Novartis, Roche, Chengdu Kanghong Biotechnology Co., Code C (Consultant/Contractor), Heidelberg Engineering, Optos, Optovue, Quantel Medical, Centervue, Carl Zeiss Meditec, Nidek, Topcon, Code F (Financial Support), Heidelberg Engineering, Carl Zeiss Meditec, Nidek, Bayer, Novartis, Roche, Code R (Recipient); Giacinto Triolo None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 2022 – A0463. doi:
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      Matteo Airaldi, Jonathan D Oakley, Sara Bochicchio, Angelica Dipinto, Simona Prandoni, Giovanni Staurenghi, Giacinto Triolo; Automated segmentation of retinal nerve fiber layer excluding retinal blood vessels: integrating OCT and OCT Angiography.. Invest. Ophthalmol. Vis. Sci. 2022;63(7):2022 – A0463.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Peripapillary retinal blood vessels (RBVs) are currently segmented within the retinal nerve fiber layer (RNFL) in commercially available OCT segmentation software, contributing to automated calculation of RNFL thickness (std-RNFLT). In this cross-sectional clinical study we developed a fully automated segmentation algorithm capable of subtracting RBVs from RNFLT using OCT Angiography (OCT-A) scans of the optic nerve head (ONH), and we compared vessel-free RNFLT (vf-RNFLT) to std-RNFLT in order to evaluate the impact of RBVs on RNFLT.

Methods : ONH scans of 22 healthy eyes and 4 explicative glaucomatous eyes were analysed. After convolutional neural network-based RNFL and disc margin segmentation, an en face OCT-A image of the RNFL plexus was generated, enhanced and thresholded. Assuming vessels’ tubularity, the RBV thickness in the axial direction was extrapolated from vessel pixels distance to the nearest non-vessel pixel. The contribution of RBV thickness was then subtracted from the std-RNFLT map to obtain vf-RNFLT. Std- and vf-RNFLT of healthy eyes were compared with t-test. The influence of covariates was analysed by a linear mixed-effects model (GLMM).

Results : In healthy subjects, average (Avg), superior (S), nasal (N), inferior (I), temporal (T) std-RNFLT and vf-RNFLT (all values in microns, mean±SD) were 111.6±25.1 vs. 104.1±21.7 (p = .035), 134.7±14.2 vs. 124.2±13.6 (p = .016), 82.9±11.3 vs. 78.2±10.5 (p = .16), 130±14.1 vs. 117±12.3 (p = .002), 98.7±11.7 vs. 96.9±12 (p = .633). The clock hour thickness analysis showed that clock hours 5, 7, 12 were significantly different (all p < .05). Peripapillary RBVs accounted for 6.3%, 7.8%, 5.6%, 10%, and 1.8%, of the Avg, S, N, I, T std-RNFLT in healthy eyes, and 9,8%, 15%, 9.4%, 12.7%, and 2.1% in glaucomatous eyes. GLMM showed no association of age, axial length and keratometry values with the difference in clock hour std- and vf-RNFLT.

Conclusions : Peripapillary RBVs account for a significant percentage of the RNFLT, if measured by commercially available OCT segmentation software. Our fully automated deep learning-based segmentation software excluding the RBVs, which are spared by glaucomatous damage, provides more accurate estimates of RNFLT, possibly improving OCT ability to detect early neural damage due to glaucoma.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

Standard (left) and vessel-free (right) RNFLT analysis of a healthy ONH.

Standard (left) and vessel-free (right) RNFLT analysis of a healthy ONH.

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